metagenome-assembly
De novo assembly of metagenomic contigs from short reads using MEGAHIT or metaSPAdes.
- License 许可
- MIT
- 最近更新
- 2026-11-03
- Stars
- ★ 4
- Forks
- 1
01 前言 Front Matter
- name
- metagenome-assembly
- description
- De novo assembly of metagenomic contigs from short reads using MEGAHIT or metaSPAdes.
02 资源文件 9
- 参考文档agents/claude.yaml298 B
- 参考文档agents/openai.yaml299 B
- 参考文档agents/README.md322 B
- 资产文件assets/.gitkeep—
- 参考文档config.yaml258 B
- 参考文档references/megahit-metaspades.md3.3 KB
- 参考文档references/README.md68 B
- 脚本scripts/batch_run.sh4.6 KB
- 脚本scripts/run.sh5.6 KB
03 SKILL.md 正文
Skill: metagenome-assembly
Use When
- The user wants to assemble metagenomic reads into contigs for downstream analysis.
- The user needs contigs for downstream binning and gene prediction.
- The user wants to compare assemblers (MEGAHIT vs metaSPAdes).
- The user has sufficient compute resources for de novo metagenomic assembly.
Inputs
- Required:
- Host-depleted FASTQ file(s) (
.fastq,.fq,.fastq.gz,.fq.gz).
- Host-depleted FASTQ file(s) (
- Optional:
--assembler STR— Assembler to use:megahitormetaspades(default:megahit).--threads N— Number of threads (default: 4).--memory N— Memory limit in GB (default: 16).--min-length N— Minimum contig length in bp (default: 1000).--kmer-sizes STR— Comma-separated k-mer sizes for metaSPAdes (e.g.,21,33,55,77).--outdir DIR— Output directory (default:assembly_results).
Workflow
- If MEGAHIT: run
megahitwith--min-contig-len,--num-cpu-threads,-mmemory. - If metaSPAdes: run
spades.py --metawith-kkmer sizes,-tthreads,-mmemory. - Filter contigs by minimum length.
- Generate assembly statistics: total contigs, total length, N50, L50, largest contig, GC content.
- Report assembly summary.
Output Contract
- Contigs FASTA — Assembled contigs filtered by minimum length (
<outdir>/contigs_min<N>bp.fasta). - Assembly statistics — N50, L50, total length, contig count, largest contig, GC percentage (
<outdir>/assembly_stats.txt).
Limits
- metaSPAdes requires significantly more memory than MEGAHIT (100-500 GB vs 10-50 GB for human gut metagenomes).
- MEGAHIT is faster and more memory-efficient; recommended as the default for most samples.
- Minimum contig length of 1000 bp is recommended for downstream binning.
- Assembly quality depends heavily on sequencing depth and community complexity.
- MEGAHIT and metaSPAdes (SPAdes) must be installed and available on
$PATH. - This skill does not perform scaffolding; output is contigs only.
- Common failure cases:
- metaSPAdes running out of memory on complex or deeply sequenced samples.
- MEGAHIT crashing due to insufficient disk space for intermediate k-mer graph files.
- Input reads still containing host contamination, inflating assembly size with host contigs.
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